Electroencephalography (EEG) has been frequently used to measure the neural activity in brain researches. In EEG based longitudinal study, repeatability of electrode positioning is important for the consistent EEG assessment over a long period of time. Conventional methods including the international 10-20 or its expanded systems have been adopted to provide a standardized electrode positioning. The methods use four principal anatomical landmarks including nasion, inion, left and right pre-auricular points as fiducial locations for electrode placement. However, the landmarks are manually identified via visual inspection or palpation, involving variations in locations of affixed electrodes and in turn alterations in measured EEG signals. In this study, we proposed an electrode guidance navigation based on markerless augmented reality visualization, which aims to enable the precise electrode placement in a cost-effective way. The presented system uses a RGB-D camera for scanning and registration of facial surface or electrodes and thereby visualizes reference and current electrode position in real time. The experimental results from the phantom study confirmed that the positioning precision of the proposed system was improved in comparison with that of the conventional 10-20 positioning system. We believe that the presented system would be a possible alternative to the conventional systems for precise electrode placement in longitudinal EEG studies. ⓒ 2017 DGIST
Table Of Contents
Ⅰ. INTRODUCTION 1 -- 1.1 Introduction of augmented reality-based surgical navigation 1 -- 1.2 Electroencephalography and electrode positioning system 2 -- 1.3 Conventional methods 3 -- 1.4 Proposed method 6 -- Ⅱ. METHODS 8 -- 2.1 Configuration of the system 8 -- 2.1.1 System overview 8 -- 2.1.2 Development environment 9 -- 2.2 Procedure for electrode guidance 10 -- 2.2.1 Preparation step 10 -- 2.2.2 Navigation step 11 -- 2.3 Real-time surface registration 13 -- 2.3.1 Iterative closest point algorithm 13 -- 2.3.2 Image-to-patient registration 16 -- 2.3.3 Adaptive initial alignment 18 -- 2.3.4 Parallel process for real-time visualization 18 -- 2.4 Experiment methods 19 -- Ⅲ. RESULTS 23 -- 3.1 Evaluation of electrode positioning accuracy 23 -- 3.2 Evaluation of real-time surface registration performance 26 -- Ⅳ. DISCUSSION AND CONCLUSION 28 -- Ⅴ. REFERENCE 30 --
